Nov 26, 2014 · The first step in our proposed framework is to generate biclusters that will be eventually used in our classification method. We will use A to ...
Jan 8, 2014 · In this paper, we present a framework to take advantage of the homogeneously expressed genes in biclusters to construct a classifier for sample ...
The proposed technique termed RBG-CD is a microarray based approach for cancer diagnosis in which the analysis of gene expression deviation from its normal ...
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Although biclustering can be used in any kind of classification problem, nowadays it is mostly used for microarray data classification. A large number of ...
present the details of applying the multivariate meta-analysis (MVMA) framework to bicluster stacks to assess the significance of the gene sets. In Chapter ...
Spectral Biclustering of Microarray Data: Coclustering Genes and ...
pmc.ncbi.nlm.nih.gov › PMC430175
Global analyses of RNA expression levels are useful for classifying genes and overall phenotypes. Often these classification problems are linked, ...
A biclustering algorithm, based on a greedy technique and enriched with a local search strategy to escape poor local minima, is proposed.
In gene expression microarray data analysis, biclustering has been demonstrated to be one of the most effective methods for discovering gene ex- pression ...
Jul 15, 2024 · Biclustering, the simultaneous clustering of rows and columns of a data matrix, has proved its effectiveness in bioinformatics due to.
Sep 19, 2022 · Biclustering algorithm is an effective tool for processing gene expression datasets. There are two kinds of data matrices, binary data and ...